**Guest Post by Willis Eschenbach**

Over at Dr. Curry’s excellent website, she’s discussing the Red and Blue Team approach. If I ran the zoo and could re-examine the climate question, I’d want to look at what I see as the central misunderstanding in the current theory of climate.

This is the mistaken idea that changes in global temperature are a linear function of changes in the top-of-atmosphere (TOA) radiation balance (usually called “forcing”).

As evidence of the centrality of this misunderstanding, I offer the fact that the climate model output global surface temperature can be emulated to great accuracy as a lagged linear transformation of the forcings. This means that in the models, everything but the forcing cancels out and the temperature is a function of the forcings and very little else. In addition, the paper laying out those claimed mathematical underpinnings is one of the more highly-cited papers in the field.

To me, this idea that the hugely complex climate system has a secret control knob with a linear and predictable response is hugely improbable on the face of it. Complex natural systems have a whole host of internal feedbacks and mechanisms that make them act in unpredictable ways. I know of no complex natural system which has anything equivalent to that.

But that’s just one of the objections to the idea that temperature slavishly follows forcing. In my post called “The Cold Equations” I discussed the rickety mathematical underpinnings of this idea. And in “The TAO That Can Be Spoken” I showed that there are times when TOA forcing increases, but the temperature decreases.

Recently I’ve been looking at what the CERES data can tell us about the question of forcing and temperature. We can look at the relationship in a couple of ways, as a time series or a long-term average. I’ll look at both. Let me start by showing how the top-of-atmosphere (TOA) radiation imbalance varies over time. Figure 1 shows three things—the raw TOA forcing data, the seasonal component of the data, and the “residual”, what remains once we remove the seasonal component.

*Figure 1. Time series, TOA radiative forcing. The top panel shows the CERES data. The middle panel shows the seasonal component, which is caused by the earth being different distances from the sun at different times of the year. The bottom panel shows the residual, what is left over after the seasonal component is subtracted from the data.*

And here is the corresponding view of the surface temperature.

*Figure 2. Time series, global average surface temperature. The top panel shows the data. The middle panel shows the seasonal component. The bottom panel shows the residual, what is left over after the seasonal component is subtracted from the data. Note the El Nino-related warming at the end of 2015.*

Now, the question of interest involves the residuals. If there is a month with unusually high TOA radiation, does it correspond with a surface warming that month? For that, we can use a scatterplot of the residuals.

From that scatterplot, we’d have to conclude that there’s little short-term correlation between months with excess forcing and months with high temperature.

Now, this doesn’t exhaust the possibilities. There could be a correlation with a time lag between cause and effect. For this, we need to look at the “cross-correlation”. This measures the correlation at a variety of lags. Since we are investigating the question of whether TOA forcing roolz or not, we need to look at the conditions where the temperature lags the TOA forcing (positive lags). Figure 4 shows the cross-correlation.

*Figure 4. Cross-correlation, TOA forcing and temperature. Temperature lagging TOA is shown as positive. In no case are the correlations even approaching significance.*

OK, so on average there’s very little correlation between TOA forcing and temperature. There’s another way we can look at the question. This is the temporal trend of TOA forcing and temperature on a 1° latitude by 1° longitude gridcell basis. Figure 5 shows that result:

There are some interesting results there. First, correlation over the land is slightly positive, and over the ocean, it is slightly negative. Half the gridcells are in the range ±0.15, very poorly correlated. Nowhere is a here strong positive correlation. On the other hand, Antarctica is strongly negatively correlated. I have no idea why.

Now, I said at the onset that there were a couple of ways to look at this relationship between surface temperature and TOA radiative balance—how it evolves over time, and how it is reflected in long-term averages. Above we’ve looked at it over time, seeing in a variety of ways if monthly changes or annual in one are reflected in the other. Now let’s look at the averages. First,…